Metadata-Version: 2.1
Name: sdv
Version: 0.7.0.dev1
Summary: Synthetic Data Generation for tabular, relational and time series data.
Home-page: https://github.com/sdv-dev/SDV
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Description: <p align="left">
          <a href="https://dai.lids.mit.edu">
            <img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt="DAI-Lab" />
          </a>
          <i>An Open Source Project from the <a href="https://dai.lids.mit.edu">Data to AI Lab, at MIT</a></i>
        </p>
        
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        <img width=30% src="docs/images/SDV-Logo-Color-Tagline.png">
        
        * Website: https://sdv.dev
        * Documentation: https://sdv.dev/SDV
            * [User Guides](https://sdv.dev/SDV/user_guides/index.html)
            * [Developer Guides](https://sdv.dev/SDV/developer_guides/index.html)
        * Github: https://github.com/sdv-dev/SDV
        * License: [MIT](https://github.com/sdv-dev/SDV/blob/master/LICENSE)
        * Development Status: [Pre-Alpha](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
        
        ## Overview
        
        The **Synthetic Data Vault (SDV)** is a **Synthetic Data Generation** ecosystem of libraries
        that allows users to easily learn [single-table](
        https://sdv.dev/SDV/user_guides/single_table/index.html), [multi-table](
        https://sdv.dev/SDV/user_guides/relational/index.html) and [timeseries](
        https://sdv.dev/SDV/user_guides/timeseries/index.html) datasets to later on generate new
        **Synthetic Data** that has the **same format and statistical properties** as the original dataset.
        
        Synthetic data can then be used to supplement, augment and in some cases replace real data when
        training Machine Learning models. Additionally, it enables the testing of Machine Learning or
        other data dependent software systems without the risk of exposure that comes with data
        disclosure.
        
        Underneath the hood it uses several probabilistic graphical modeling and deep learning based
        techniques. To enable a variety of data storage structures, we employ unique
        hierarchical generative modeling and recursive sampling techniques.
        
        ### Current functionality and features:
        
        * Synthetic data generators for [single tables](
          https://sdv.dev/SDV/user_guides/single_table/index.html) with the following
          features:
            * Using [Copulas](https://sdv.dev/SDV/user_guides/single_table/gaussian_copula.html)
              and [Deep Learning](https://sdv.dev/SDV/user_guides/single_table/ctgan.html)
              based models.
            * Handling of multiple data types and missing data with minimum user input.
            * Support for [pre-defined and custom constraints](
              https://sdv.dev/SDV/user_guides/single_table/constraints.html) and data validation.
        * Synthetic data generators for [complex multi-table, relational datasets](
          https://sdv.dev/SDV/user_guides/relational/index.html) with the following
          features:
            * Definition of entire [multi-table datasets metadata](
              https://sdv.dev/SDV/user_guides/relational/relational_metadata.html) with a custom and
              flexible [JSON schema](https://sdv.dev/SDV/developer_guides/sdv/metadata.html).
            * Using Copulas and recursive modeling techniques.
        * Synthetic data generators for [multi-type, multi-variate timeseries](
          https://sdv.dev/SDV/user_guides/timeseries/index.html) with the following features:
            * Using statistical, Autoregressive and Deep Learning models.
            * Conditional sampling based on contextual attributes.
        
        ## Try it out now!
        
        If you want to quickly discover **SDV**, simply click the button below and follow the tutorials!
        
        [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sdv-dev/SDV/master?filepath=tutorials)
        
        ## Join our Slack Workspace
        
        If you want to be part of the SDV community to receive announcements of the latest releases,
        ask questions, suggest new features or participate in the development meetings, please join
        our Slack Workspace!
        
        [![Slack](https://img.shields.io/badge/Slack%20Workspace-Join%20now!-36C5F0?logo=slack)](https://join.slack.com/t/sdv-space/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw)
        
        # Install
        
        **Using `pip`:**
        
        ```bash
        pip install sdv
        ```
        
        **Using `conda`:**
        
        ```bash
        conda install -c sdv-dev -c conda-forge sdv
        ```
        
        For more installation options please visit the [SDV installation Guide](
        https://sdv.dev/SDV/getting_started/install.html)
        
        # Quickstart
        
        In this short tutorial we will guide you through a series of steps that will help you
        getting started using **SDV**.
        
        ## 1. Model the dataset using SDV
        
        To model a multi table, relational dataset, we follow two steps. In the first step, we will load
        the data and configures the meta data. In the second step, we will use the sdv API to fit and
        save a hierarchical model. We will cover these two steps in this section using an example dataset.
        
        ### Step 1: Load example data
        
        **SDV** comes with a toy dataset to play with, which can be loaded using the `sdv.load_demo`
        function:
        
        ```python3
        from sdv import load_demo
        
        metadata, tables = load_demo(metadata=True)
        ```
        
        This will return two objects:
        
        1. A `Metadata` object with all the information that **SDV** needs to know about the dataset.
        
        For more details about how to build the `Metadata` for your own dataset, please refer to the
        [Working with Metadata](https://sdv.dev/SDV/user_guides/relational/relational_metadata.html)
        tutorial.
        
        2. A dictionary containing three `pandas.DataFrames` with the tables described in the
        metadata object.
        
        The returned objects contain the following information:
        
        ```
        {
            'users':
                    user_id country gender  age
                  0        0     USA      M   34
                  1        1      UK      F   23
                  2        2      ES   None   44
                  3        3      UK      M   22
                  4        4     USA      F   54
                  5        5      DE      M   57
                  6        6      BG      F   45
                  7        7      ES   None   41
                  8        8      FR      F   23
                  9        9      UK   None   30,
          'sessions':
                  session_id  user_id  device       os
                  0           0        0  mobile  android
                  1           1        1  tablet      ios
                  2           2        1  tablet  android
                  3           3        2  mobile  android
                  4           4        4  mobile      ios
                  5           5        5  mobile  android
                  6           6        6  mobile      ios
                  7           7        6  tablet      ios
                  8           8        6  mobile      ios
                  9           9        8  tablet      ios,
          'transactions':
                  transaction_id  session_id           timestamp  amount  approved
                  0               0           0 2019-01-01 12:34:32   100.0      True
                  1               1           0 2019-01-01 12:42:21    55.3      True
                  2               2           1 2019-01-07 17:23:11    79.5      True
                  3               3           3 2019-01-10 11:08:57   112.1     False
                  4               4           5 2019-01-10 21:54:08   110.0     False
                  5               5           5 2019-01-11 11:21:20    76.3      True
                  6               6           7 2019-01-22 14:44:10    89.5      True
                  7               7           8 2019-01-23 10:14:09   132.1     False
                  8               8           9 2019-01-27 16:09:17    68.0      True
                  9               9           9 2019-01-29 12:10:48    99.9      True
        }
        ```
        
        ### 2. Fit a model using the SDV API.
        
        First, we build a hierarchical statistical model of the data using **SDV**. For this we will
        create an instance of the `sdv.SDV` class and use its `fit` method.
        
        During this process, **SDV** will traverse across all the tables in your dataset following the
        primary key-foreign key relationships and learn the probability distributions of the values in
        the columns.
        
        ```python3
        from sdv import SDV
        
        sdv = SDV()
        sdv.fit(metadata, tables)
        ```
        
        Once the modeling has finished, you can save your fitted `SDV` instance for later usage
        using the `save` method of your instance.
        
        ```python3
        sdv.save('sdv.pkl')
        ```
        
        The generated `pkl` file will not include any of the original data in it, so it can be
        safely sent to where the synthetic data will be generated without any privacy concerns.
        
        ## 2. Sample data from the fitted model
        
        In order to sample data from the fitted model, we will first need to load it from its
        `pkl` file. Note that you can skip this step if you are running all the steps sequentially
        within the same python session.
        
        ```python3
        sdv = SDV.load('sdv.pkl')
        ```
        
        After loading the instance, we can sample synthetic data by calling its `sample` method.
        
        ```python3
        samples = sdv.sample()
        ```
        
        The output will be a dictionary with the same structure as the original `tables` dict,
        but filled with synthetic data instead of the real one.
        
        Finally, if you want to evaluate how similar the sampled tables are to the real data,
        please have a look at our [evaluation](EVALUATION.md) framework or visit the [SDMetrics](
        https://github.com/sdv-dev/SDMetrics) library.
        
        # Join our community
        
        1. If you would like to see more usage examples, please have a look at the [tutorials folder](
           https://github.com/sdv-dev/SDV/tree/master/tutorials) of the repository. Please contact us
           if you have a usage example that you would want to share with the community.
        2. Please have a look at the [Contributing Guide](https://sdv.dev/SDV/developer_guides/contributing.html)
           to see how you can contribute to the project.
        3. If you have any doubts, feature requests or detect an error, please [open an issue on github](
           https://github.com/sdv-dev/SDV/issues) or [join our Slack Workspace](
           https://join.slack.com/t/sdv-space/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw)
        4. Also, do not forget to check the [project documentation site](https://sdv.dev/SDV/)!
        
        # Citation
        
        If you use **SDV** for your research, please consider citing the following paper:
        
        Neha Patki, Roy Wedge, Kalyan Veeramachaneni. [The Synthetic Data Vault](https://dai.lids.mit.edu/wp-content/uploads/2018/03/SDV.pdf). [IEEE DSAA 2016](https://ieeexplore.ieee.org/document/7796926).
        
        ```
        @inproceedings{
            7796926,
            author={N. {Patki} and R. {Wedge} and K. {Veeramachaneni}},
            booktitle={2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)},
            title={The Synthetic Data Vault},
            year={2016},
            volume={},
            number={},
            pages={399-410},
            keywords={data analysis;relational databases;synthetic data vault;SDV;generative model;relational database;multivariate modelling;predictive model;data analysis;data science;Data models;Databases;Computational modeling;Predictive models;Hidden Markov models;Numerical models;Synthetic data generation;crowd sourcing;data science;predictive modeling},
            doi={10.1109/DSAA.2016.49},
            ISSN={},
            month={Oct}
        }
        ```
        
        
        # Release Notes
        
        ## 0.7.0 - 2021-01-27
        
        This release introduces a few changes in the HMA1 relational algorithm to decrease modeling
        and sampling times, while also ensuring that correlations are properly kept across tables
        and also adding support for some relational schemas that were not supported before.
        
        A few changes in constraints and tabular models also ensure that situations that produced
        errors before now work without errors.
        
        ### Issues resolved
        
        * Fix unique key generation - Issue [#306](https://github.com/sdv-dev/SDV/issues/306) by @fealho
        * Ensure tables that contain nothing but ids can be modeled - Issue [#302](https://github.com/sdv-dev/SDV/issues/302) by @csala
        * Metadata visualization improvements - Issue [#301](https://github.com/sdv-dev/SDV/issues/301) by @csala
        * Multi-parent re-model and re-sample issue - Issue [#298](https://github.com/sdv-dev/SDV/issues/298) by @csala
        * Support datetimes in GreaterThan constraint - Issue [#266](https://github.com/sdv-dev/SDV/issues/266) by @rollervan
        * Support for multiple foreign keys in one table - Issue [#185](https://github.com/sdv-dev/SDV/issues/185) by @csala
        
        ## 0.6.1 - 2020-12-31
        
        SDMetrics version is updated to include the new Time Series metrics, which have also
        been added to the API Reference and User Guides documentation. Additionally,
        a few code has been refactored to reduce external dependencies and a few minor bugs
        related to single table constraints have been fixed
        
        ### Issues resolved
        
        * Add timeseries metrics and user guides - [Issue #289](https://github.com/sdv-dev/SDV/issues/289) by @csala
        * Add functions to generate regex ids - [Issue #288](https://github.com/sdv-dev/SDV/issues/288) by @csala
        * Saving a fitted tabular model with UniqueCombinations constraint raises PicklingError -
          [Issue #286](https://github.com/sdv-dev/SDV/issues/288) by @csala
        * Constraints: `handling_strategy='reject_sampling'` causes `'ZeroDivisionError: division by zero'` -
          [Issue #285](https://github.com/sdv-dev/SDV/issues/285) by @csala
        
        ## 0.6.0 - 2020-12-22
        
        This release updates to the latest CTGAN, RDT and SDMetrics libraries to introduce a
        new TVAE model, multiple new metrics for single table and multi table, and fixes
        issues in the re-creation of tabular models from a metadata dict.
        
        ### Issues resolved
        
        * Upgrade to SDMetrics v0.1.0 and add `sdv.metrics` module - [Issue #281](https://github.com/sdv-dev/SDV/issues/281) by @csala
        * Upgrade to CTGAN 0.3.0 and add TVAE model - [Issue #278](https://github.com/sdv-dev/SDV/issues/278) by @fealho
        * Add `dtype_transformers` to `Table.from_dict` - [Issue #276](https://github.com/sdv-dev/SDV/issues/276) by @csala
        * Fix Metadata `from_dict` behavior - [Issue #275](https://github.com/sdv-dev/SDV/issues/275) by @csala
        
        ## 0.5.0 - 2020-11-25
        
        This version updates the dependencies and makes a few internal changes in order
        to ensure that SDV works properly on Windows Systems, making this the first
        release to be officially supported on Windows.
        
        Apart from this, some more internal changes have been made to solve a few minor
        issues from the older versions while also improving the processing speed when
        processing relational datasets with the default parameters.
        
        ### API breaking changes
        
        * The `distribution` argument of the `GaussianCopula` has been renamed to `field_distributions`.
        * The `HMA1` and `SDV` classes now use the `categorical_fuzzy` transformer by default instead of
          the `one_hot_encoding` one.
        
        ### Issues resolved
        
        * GaussianCopula: rename `distribution` argument to `field_distributions` - [Issue #237](https://github.com/sdv-dev/SDV/issues/237) by @csala
        * GaussianCopula: Improve error message if an invalid distribution name is passed - [Issue #220](https://github.com/sdv-dev/SDV/issues/220) by csala
        * Import urllib.request explicitly - [Issue #227](https://github.com/sdv-dev/SDV/issues/227) by @csala
        * TypeError: cannot astype a datetimelike from [datetime64[ns]] to [int32] - [Issue #218](https://github.com/sdv-dev/SDV/issues/218) by @csala
        * Change default categorical transformer to `categorical_fuzzy` in HMA1 - [Issue #214](https://github.com/sdv-dev/SDV/issues/214) by @csala
        * Integer categoricals being sampled as strings instead of integer values - [Issue #194](https://github.com/sdv-dev/SDV/issues/194) by @csala
        
        ## 0.4.5 - 2020-10-17
        
        In this version a new family of models for Synthetic Time Series Generation is introduced
        under the `sdv.timeseries` sub-package. The new family of models now includes a new class
        called `PAR`, which implements a *Probabilistic AutoRegressive* model.
        
        This version also adds support for composite primary keys and regex based generation of id
        fields in tabular models and drops Python 3.5 support.
        
        ### Issues resolved
        
        * Drop python 3.5 support - [Issue #204](https://github.com/sdv-dev/SDV/issues/204) by @csala
        * Support composite primary keys in tabular models - [Issue #207](https://github.com/sdv-dev/SDV/issues/207) by @csala
        * Add the option to generate string `id` fields based on regex on tabular models - [Issue #208](https://github.com/sdv-dev/SDV/issues/208) by @csala
        * Synthetic Time Series - [Issue #142](https://github.com/sdv-dev/SDV/issues/142) by @csala
        
        
        ## 0.4.4 - 2020-10-06
        
        This version adds a new tabular model based on combining the CTGAN model with the reversible
        transformation applied in the GaussianCopula model that converts random variables with
        arbitrary distributions to new random variables with standard normal distribution.
        
        The reversible transformation is handled by the GaussianCopulaTransformer recently added to RDT.
        
        ### Issues resolved
        
        * Add CopulaGAN Model - [Issue #202](https://github.com/sdv-dev/SDV/issues/202) by @csala
        
        ## 0.4.3 - 2020-09-28
        
        This release moves the models and algorithms related to generation of synthetic
        relational data to a new `sdv.relational` subpackage (Issue #198)
        
        As part of the change, also the old `sdv.models` have been removed and now
        relational model is based on the recently introduced `sdv.tabular` models.
        
        ## 0.4.2 - 2020-09-19
        
        In this release the `sdv.evaluation` module has been reworked to include 4 different
        metrics and in all cases return a normalized score between 0 and 1.
        
        Included metrics are:
        - `cstest`
        - `kstest`
        - `logistic_detection`
        - `svc_detection`
        
        ## 0.4.1 - 2020-09-07
        
        This release fixes a couple of minor issues and introduces an important rework of the
        User Guides section of the documentation.
        
        ### Issues fixed
        
        * Error Message: "make sure the Graphviz executables are on your systems' PATH" - [Issue #182](https://github.com/sdv-dev/SDV/issues/182) by @csala
        * Anonymization mappings leak - [Issue #187](https://github.com/sdv-dev/SDV/issues/187) by @csala
        
        ## 0.4.0 - 2020-08-08
        
        In this release SDV gets new documentation, new tutorials, improvements to the Tabular API
        and broader python and dependency support.
        
        Complete list of changes:
        
        * New Documentation site based on the `pydata-sphinx-theme`.
        * New User Guides and Notebook tutorials.
        * New Developer Guides section within the docs with details about the SDV architecture,
          the ecosystem libraries and how to extend and contribute to the project.
        * Improved API for the Tabular models with focus on ease of use.
        * Support for Python 3.8 and the newest versions of pandas, scipy and scikit-learn.
        * New Slack Workspace for development discussions and community support.
        
        ## 0.3.6 - 2020-07-23
        
        This release introduces a new concept of `Constraints`, which allow the user to define
        special relationships between columns that will not be handled via modeling.
        
        This is done via a new `sdv.constraints` subpackage which defines some well-known pre-defined
        constraints, as well as a generic framework that allows the user to customize the constraints
        to their needs as much as necessary.
        
        ### New Features
        
        * Support for Constraints - [Issue #169](https://github.com/sdv-dev/SDV/issues/169) by @csala
        
        
        ## 0.3.5 - 2020-07-09
        
        This release introduces a new subpackage `sdv.tabular` with models designed specifically
        for single table modeling, while still providing all the usual conveniences from SDV, such
        as:
        
        * Seamless multi-type support
        * Missing data handling
        * PII anonymization
        
        Currently implemented models are:
        
        * GaussianCopula: Multivariate distributions modeled using copula functions. This is stronger
          version, with more marginal distributions and options, than the one used to model multi-table
          datasets.
        * CTGAN: GAN-based data synthesizer that can generate synthetic tabular data with high fidelity.
        
        
        ## 0.3.4 - 2020-07-04
        
        ### New Features
        
        * Support for Multiple Parents - [Issue #162](https://github.com/sdv-dev/SDV/issues/162) by @csala
        * Sample by default the same number of rows as in the original table - [Issue #163](https://github.com/sdv-dev/SDV/issues/163) by @csala
        
        ### General Improvements
        
        * Add benchmark - [Issue #165](https://github.com/sdv-dev/SDV/issues/165) by @csala
        
        ## 0.3.3 - 2020-06-26
        
        ### General Improvements
        
        * Use SDMetrics for evaluation - [Issue #159](https://github.com/sdv-dev/SDV/issues/159) by @csala
        
        ## 0.3.2 - 2020-02-03
        
        ### General Improvements
        
        * Improve metadata visualization - [Issue #151](https://github.com/sdv-dev/SDV/issues/151) by @csala @JDTheRipperPC
        
        ## 0.3.1 - 2020-01-22
        
        ### New Features
        
        * Add Metadata Validation - [Issue #134](https://github.com/sdv-dev/SDV/issues/134) by @csala @JDTheRipperPC
        
        * Add Metadata Visualization - [Issue #135](https://github.com/sdv-dev/SDV/issues/135) by @JDTheRipperPC
        
        ### General Improvements
        
        * Add path to metadata JSON - [Issue #143](https://github.com/sdv-dev/SDV/issues/143) by @JDTheRipperPC
        
        * Use new Copulas and RDT versions - [Issue #147](https://github.com/sdv-dev/SDV/issues/147) by @csala @JDTheRipperPC
        
        ## 0.3.0 - 2019-12-23
        
        ### New Features
        
        * Create sdv.models subpackage - [Issue #141](https://github.com/sdv-dev/SDV/issues/141) by @JDTheRipperPC
        
        ## 0.2.2 - 2019-12-10
        
        ### New Features
        
        * Adapt evaluation to the different data types - [Issue #128](https://github.com/sdv-dev/SDV/issues/128) by @csala @JDTheRipperPC
        
        * Extend `load_demo` functionality to load other datasets - [Issue #136](https://github.com/sdv-dev/SDV/issues/136) by @JDTheRipperPC
        
        ## 0.2.1 - 2019-11-25
        
        ### New Features
        
        * Methods to generate Metadata from DataFrames - [Issue #126](https://github.com/sdv-dev/SDV/issues/126) by @csala @JDTheRipperPC
        
        ## 0.2.0 - 2019-10-11
        
        ### New Features
        
        * compatibility with rdt issue 72 - [Issue #120](https://github.com/sdv-dev/SDV/issues/120) by @csala @JDTheRipperPC
        
        ### General Improvements
        
        * Error docstring sampler.__fill_text_columns - [Issue #144](https://github.com/sdv-dev/SDV/issues/114) by @JDTheRipperPC
        * Reach 90% coverage - [Issue #112](https://github.com/sdv-dev/SDV/issues/112) by @JDTheRipperPC
        * Review unittests - [Issue #111](https://github.com/sdv-dev/SDV/issues/111) by @JDTheRipperPC
        
        ### Bugs Fixed
        
        * Time required for sample_all function? - [Issue #118](https://github.com/sdv-dev/SDV/issues/118) by @csala @JDTheRipperPC
        
        ## 0.1.2 - 2019-09-18
        
        ### New Features
        
        * Add option to model the amount of child rows - Issue [93](https://github.com/sdv-dev/SDV/issues/93) by @ManuelAlvarezC
        
        ### General Improvements
        
        * Add Evaluation Metrics - Issue [52](https://github.com/sdv-dev/SDV/issues/52) by @ManuelAlvarezC
        
        * Ensure unicity on primary keys on different calls - Issue [63](https://github.com/sdv-dev/SDV/issues/63) by @ManuelAlvarezC
        
        ### Bugs fixed
        
        * executing readme: 'not supported between instances of 'int' and 'NoneType' - Issue [104](https://github.com/sdv-dev/SDV/issues/104) by @csala
        
        ## 0.1.1 - Anonymization of data
        
        * Add warnings when trying to model an unsupported dataset structure. [GH#73](https://github.com/sdv-dev/SDV/issues/73)
        * Add option to anonymize data. [GH#51](https://github.com/sdv-dev/SDV/issues/51)
        * Add support for modeling data with different distributions, when using `GaussianMultivariate` model. [GH#68](https://github.com/sdv-dev/SDV/issues/68)
        * Add support for `VineCopulas` as a model. [GH#71](https://github.com/sdv-dev/SDV/issues/71)
        * Improve `GaussianMultivariate` parameter sampling, avoiding warnings and unvalid parameters. [GH#58](https://github.com/sdv-dev/SDV/issues/58)
        * Fix issue that caused that sampled categorical values sometimes got numerical values mixed. [GH#81](https://github.com/sdv-dev/SDV/issues/81)
        * Improve the validation of extensions. [GH#69](https://github.com/sdv-dev/SDV/issues/69)
        * Update examples. [GH#61](https://github.com/sdv-dev/SDV/issues/61)
        * Replaced `Table` class with a `NamedTuple`. [GH#92](https://github.com/sdv-dev/SDV/issues/92)
        * Fix inconsistent dependencies and add upper bound to dependencies. [GH#96](https://github.com/sdv-dev/SDV/issues/96)
        * Fix error when merging extension in `Modeler.CPA` when running examples. [GH#86](https://github.com/sdv-dev/SDV/issues/86)
        
        ## 0.1.0 - First Release
        
        * First release on PyPI.
        
Keywords: sdv synthetic-data synhtetic-data-generation timeseries single-table multi-table
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6,<3.9
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
